Space-time sampling strategies for electronically steerable incoherent scatter radar

Incoherent scatter radar (ISR) systems allow researchers to peer into the ionosphere via remote sensing of intrinsic plasma parameters. ISR sensors have been used since the 1950s and until the past decade were mainly equipped with a single mechanically steerable antenna. As such, the ability to develop a two or three dimensional picture of the plasma parameters in the ionosphere has been constrained by the relatively slow mechanical steering of the antennas. A newer class of systems using electronically steerable array (ESA) antennas have broken the chains of this constraint, allowing researchers to create 3-D reconstructions of plasma parameters. There have been many studies associated with reconstructing 3-D fields of plasma parameters, but there has not been a systematic analysis into the sampling issues that arise. Also, there has not been a systematic study as to how to reconstruct these plasma parameters in an optimum sense as opposed to just using different forms of interpolation. The research presented here forms a framework that scientists and engineers can use to plan experiments with ESA ISR capabilities and to better analyze the resulting data. This framework attacks the problem of space-time sampling by ESA ISR systems from the point of view of signal processing, simulation and inverse theoretic image reconstruction. We first describe a physics based model of incoherent scatter from the ionospheric plasma, along with processing methods needed to create the plasma parameter measurements. Our approach leads to development of the space-time ambiguity function, forming a theoretical foundation of the forward model for ISR. This forward model is novel in that it takes into account the shape of the antenna beam and scanning method along with integration time to develop the proper statistics for a desired measurement precision. Once the forward model is developed, we present the simulation method behind the Simulator for ISR (SimISR). SimISR uses input plasma parameters over space and time and creates complex voltage samples in a form similar to that produced by a real ISR system. SimISR allows researchers to evaluate different experiment configurations in order to efficiently and accurately sample specific phenomena. We present example simulations using input conditions derived from a multi-fluid ionosphere model and reconstructions using standard interpolation techniques. Lastly, methods are presented to invert the space-time ambiguity function using techniques from image reconstruction literature. These methods are tested using SimISR to quantify accurate plasma parameter reconstruction over a simulated ionospheric region

Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked Autoencoders

Radio frequency data in astronomy enable scientists to analyze astrophysical phenomena. However, these data can be corrupted by radio frequency interference (RFI) that limits the observation of underlying natural processes. In this study, we extend recent developments in deep learning algorithms to astronomy data. We remove RFI from time-frequency spectrograms containing auroral kilometric radiation (AKR), a coherent radio emission originating from the Earth's auroral zones that is used to study astrophysical plasmas. We propose a Denoising Autoencoder for Auroral Radio Emissions (DAARE) trained with synthetic spectrograms to denoise AKR signals collected at the South Pole Station. DAARE achieves 42.2 peak signal-to-noise ratio (PSNR) and 0.981 structural similarity (SSIM) on synthesized AKR observations, improving PSNR by 3.9 and SSIM by 0.064 compared to state-of-the-art filtering and denoising networks. Qualitative comparisons demonstrate DAARE's capability to effectively remove RFI from real AKR observations, despite being trained completely on a dataset of simulated AKR. The framework for simulating AKR, training DAARE, and employing DAARE can be accessed at github.com/Cylumn/daare.Comment: 5 pages, 3 figures, 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

AERO & VISTA: Demonstrating HF Radio Interferometry with Vector Sensors

AERO (Auroral Emission Radio Observer) and VISTA (Vector Interferometry Space Technology using AERO) are recently selected NASA HTIDeS CubeSat missions for terrestrial auroral science and radio interferometric technology demonstration. The AERO and VISTA CubeSats both host vector sensing antenna systems providing advanced electromagnetic capabilities. Together, they will provide the first in-space demonstration of interferometric imaging, beamforming, and nulling using electromagnetic vector sensors at low frequencies (100 kHz –15 MHz). A key goal of the joint missions’ technology demonstration is to validate theoretical sensor performance modeling indicating that interferometric arrays composed of vector sensors will be able to maintain sensitivity even in the presence of terrestrial interference. If validated in flight, this capability would relax the requirement that space-based low frequency interferometers be placed far from the Earth (e.g. lunar orbit), and the closer communications range will significantly increase the data volume returned from space-based radio telescope systems. The two-spacecraft AERO+VISTA mission will address the auroral science goals of AERO (Erickson et al. 2018, SSC18) while adding three additional technology demonstration goals enabled by the second CubeSat, VISTA

The role of left ventricular deformation in the assessment of microvascular obstruction and intramyocardial haemorrhage

In the setting of acute ST-elevation myocardial infarction (STEMI), it remains unclear which strain parameter most strongly correlates with microvascular obstruction (MVO) or intramyocardial haemorrhage (IMH). We aimed to investigate the association of MVO, IMH and convalescent left ventricular (LV) remodelling with strain parameters measured with cardiovascular magnetic resonance (CMR). Forty-three patients with reperfused STEMI and 10 age and gender matched healthy controls underwent CMR within 3-days and at 3-months following reperfused STEMI. Cine, T2-weighted, T2*-imaging and late gadolinium enhancement (LGE) imaging were performed. Infarct size, MVO and IMH were quantified. Peak global longitudinal strain (GLS), global radial strain (GRS), global circumferential strain (GCS) and their strain rates were derived by feature tracking analysis of LV short-axis, 4-chamber and 2-chamber cines. All 43 patients and ten controls completed the baseline scan and 34 patients completed 3-month scans. In multivariate regression, GLS demonstrated the strongest association with MVO or IMH (beta = 0.53, p 20%). Baseline GLS also demonstrated the strongest diagnostic performance in predicting adverse LV remodelling (AUC = 0.79; 95% CI 0.60–0.98; p = 0.03). Post-reperfused STEMI, baseline GLS was most closely associated with the presence of MVO or IMH. Baseline GLS was more strongly associated with adverse LV remodelling than other CMR parameters

Acute Infarct Extracellular Volume Mapping to Quantify Myocardial Area at Risk and Chronic Infarct Size on Cardiovascular Magnetic Resonance Imaging

Background—Late gadolinium enhancement (LGE) imaging overestimates acute infarct size. The main aim of this study was to investigate whether acute extracellular volume (ECV) maps can reliably quantify myocardial area at risk (AAR) and final infarct size (IS). Methods and Results—Fifty patients underwent cardiovascular magnetic resonance imaging acutely (24–72 hours) and at convalescence (3 months). The cardiovascular magnetic resonance protocol included cines, T2-weighted imaging, native T1 maps, 15-minute post-contrast T1 maps, and LGE. Optimal AAR and IS ECV thresholds were derived in a validation group of 10 cases (160 segments). Eight hundred segments (16 per patient) were analyzed to quantify AAR/IS by ECV maps (ECV thresholds for AAR is 33% and IS is 46%), T2-weighted imaging, T1 maps, and acute LGE. Follow-up LGE imaging was used as the reference standard for final IS and viability assessment. The AAR derived from ECV maps (threshold of >33) demonstrated good agreement with T2-weighted imaging–derived AAR (bias, 0.18; 95% confidence interval [CI], −1.6 to 1.3) and AAR derived from native T1 maps (bias=1; 95% CI, −0.37 to 2.4). ECV demonstrated the best linear correlation to final IS at a threshold of >46% (R=0.96; 95% CI, 0.92–0.98; P<0.0001). ECV maps demonstrated better agreement with final IS than acute IS on LGE (ECV maps: bias, 1.9; 95% CI, 0.4–3.4 versus LGE imaging: bias, 10; 95% CI, 7.7–12.4). On multiple variable regression analysis, the number of nonviable segments was independently associated with IS by ECV maps (β=0.86; P<0.0001). Conclusions—ECV maps can reliably quantify AAR and final IS in reperfused acute myocardial infarction. Acute ECV maps were superior to acute LGE in terms of agreement with final IS. IS quantified by ECV maps are independently associated with viability at follow-up

The impact of trans-catheter aortic valve replacement induced leftbundle branch block on cardiac reverse remodeling

Background Left bundle branch block (LBBB) is common following trans-catheter aortic valve replacement (TAVR) and has been linked to increased mortality, although whether this is related to less favourable cardiac reverse remodeling is unclear. The aim of the study was to investigate the impact of TAVR induced LBBB on cardiac reverse remodeling. Methods 48 patients undergoing TAVR for severe aortic stenosis were evaluated. 24 patients with new LBBB (LBBB-T) following TAVR were matched with 24 patients with a narrow post-procedure QRS (nQRS). Patients underwent cardiovascular magnetic resonance (CMR) prior to and 6 m post-TAVR. Measured cardiac reverse remodeling parameters included left ventricular (LV) size, ejection fraction (LVEF) and global longitudinal strain (GLS). Inter- and intra-ventricular dyssynchrony were determined using time to peak radial strain derived from CMR Feature Tracking. Results In the LBBB-T group there was an increase in QRS duration from 96 ± 14 to 151 ± 12 ms (P < 0.001) leading to inter- and intra-ventricular dyssynchrony (inter: LBBB-T 130 ± 73 vs nQRS 23 ± 86 ms, p < 0.001; intra: LBBB-T 118 ± 103 vs. nQRS 13 ± 106 ms, p = 0.001). Change in indexed LV end-systolic volume (LVESVi), LVEF and GLS was significantly different between the two groups (LVESVi: nQRS -7.9 ± 14.0 vs. LBBB-T -0.6 ± 10.2 ml/m2, p = 0.02, LVEF: nQRS +4.6 ± 7.8 vs LBBB-T -2.1 ± 6.9%, p = 0.002; GLS: nQRS -2.1 ± 3.6 vs. LBBB-T +0.2 ± 3.2%, p = 0.024). There was a significant correlation between change in QRS and change in LVEF (r = -0.434, p = 0.002) and between change in QRS and change in GLS (r = 0.462, p = 0.001). Post-procedure QRS duration was an independent predictor of change in LVEF and GLS at 6 months. Conclusion TAVR-induced LBBB is associated with less favourable cardiac reverse remodeling at medium term follow up. In view of this, every effort should be made to prevent TAVR-induced LBBB, especially as TAVR is now being extended to a younger, lower risk population

Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: a comprehensive review.

Cardiovascular Magnetic Resonance is increasingly used to differentiate the aetiology of cardiomyopathies. Late Gadolinium Enhancement (LGE) is the reference standard for non-invasive imaging of myocardial scar and focal fibrosis and is valuable in the differential diagnosis of ischaemic versus non-ischaemic cardiomyopathy. Diffuse fibrosis may go undetected on LGE imaging. Tissue characterisation with parametric mapping methods has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by LGE. Native and post-contrast T1 mapping in particular has shown promise as a novel biomarker to support diagnostic, therapeutic and prognostic decision making in ischaemic and non-ischaemic cardiomyopathies as well as in patients with acute chest pain syndromes. Furthermore, changes in the myocardium over time may be assessed longitudinally with this non-invasive tissue characterisation method